NLP Models for Sentiment Analysis of Twitter Data

Abstract

Although significant advancements have been made in tweet sentiment analysis—particularly with the rise of deep learning and the availability of large, diverse training datasets—research in this area has often remained focused on traditional forms of content such as movie and product reviews. While recent datasets extend beyond basic emoticons and hashtags, many earlier sentiment analysis studies have primarily addressed tweets with only two sentiment polarities: positive and negative. Moreover, these systems frequently fail to align sentiment classifications with specific target entities or topics. In this paper, we explore several deep learning techniques for sentiment analysis on Twitter data. We also trained models using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), achieving promising accuracy in sentiment classification

Introduction

Sentiment analysis, or opinion mining, is an automated technique used to interpret opinions expressed in text or speech about specific subjects. With the explosion of data generated daily, it helps businesses extract valuable insights from unstructured text across platforms like social media, forums, and review sites. Twitter, with millions of daily active users and tweets, is a key source for real-time sentiment analysis, enabling companies to monitor public opinion, manage brand reputation, and respond promptly to customer feedback.

The process involves identifying the subject of discussion, the polarity of sentiment (positive, negative, or neutral), and the opinion holder. Machine learning and natural language processing (NLP) advances, particularly deep learning, have improved the efficiency and accuracy of sentiment classification by automating feature extraction and handling vast datasets.

Key benefits of sentiment analysis include time efficiency, real-time insights, improved decision-making, scalability, and consistent evaluation, especially when applied to Twitter data.

The literature review highlights the evolution from classical machine learning to deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Recursive Neural Networks (RecNNs). CNNs, widely successful in NLP, detect local patterns in text, while RNNs and RecNNs handle sequential and hierarchical language structures, respectively. Various studies have demonstrated high accuracy in sentiment classification using these deep learning architectures on Twitter datasets.

The methodology section details how these neural networks work—especially CNNs—emphasizing their layered structure (convolutional, pooling, flattening, and dense layers) to classify tweet sentiments effectively.

Overall, Twitter sentiment analysis combines deep learning with NLP to deliver scalable, real-time, and reliable insights for businesses and researchers, driving smarter engagement and market understanding.

Conclusion

In this research, a customized deep neural network framework was proposed for sentiment analysis of Twitter data. The developed model demonstrated improved performance compared to existing approaches, despite utilizing a limited number of layers and requiring lower computational resources. This highlights the effectiveness of the proposed framework in achieving a balance between computational efficiency and prediction accuracy.The results confirm that deep learning techniques can successfully handle the inherently complex nature of sentiment analysis tasks. However, there is still room for enhancement. Future improvements could include integrating techniques such as batch normalization to further increase the model\'s accuracy and generalization capability. Overall, the findings of this study support the idea that deep learning frameworks are well-suited for tackling complex sentiment analysis problems, especially those involving unstructured and large-scale data from social media platforms like Twitter

References

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Copyright

Copyright © 2025 Bhumika Sablok, Ankush Tyagi, Dharmander , Mr. Harendra Singh Malik, Dr. Saumya Chaturvedi, Dr. Sureshwati . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.